Personal / startup package
For the first hundred customers, and the systems that keep them.
- Online presence
- CRM and inventory management
- Strategy support
- Reporting you actually read
Business intelligence, played forward
BusinessIntellOffense
Take the offensive.
Data engineering, data science, AI, and the systems that have to hold them up. We play the scenario out in-house before any of it reaches your production.
Method
Three steps, no literature review
There is no need to research the field before something ships, because we are the research.
- 01
Consult
Describe the outcome you need. We size it on the same call, and say plainly when the answer is "not us".
- 02
Integrate
We wire the solution into what already runs: your data sources, your stack, your permissions, your people.
- 03
Deploy
Delivery in as little as 48 hours, then monitoring that catches the drift long before it costs you anything.
Good teams stall for good reasons: systems that were never meant to move, context held in a handful of heads, a window that narrows while the decision is still open. That is the shape of the work, not the people in it. So we run the scenarios early, in-house, several deep, at a high baseline.
Systems and formats we already work with
- SAP
- Salesforce
- Google Analytics
- Zoho
- SQL / Postgres
- SQLite .db
- REST APIs
- CSV / Excel
- SSH
- FTP
- Web scraping
Objections
What we hear across the table
Four things every buyer says. We have heard each one before, and answered it in advance.
- Said to us
“We cannot fund a whole department of directors, managers, consultants and engineers.”
Our price lands at less than a single hire, yet built like a squad.
- Said to us
“Cutting-edge solutions mean a long, painful transition.”
We run the transition with you: the methods, the handover, the documentation. Nothing left to budget for.
- Said to us
“We do not have time to rebuild anything.”
First delivery in as little as 48 hours. We have built most of this before, somewhere else.
- Said to us
“Our in-house options are running out.”
Our solutions bolt onto what you already have and add throughput, not another queue to manage.
Packages
Pick a play
Three shapes we have run before. We cut one to fit on the first call, then tell you what it costs and what it changes.
ML package
For models that scored in practice and now have to play a game.
- Server administration
- Data engineering and science pipeline
- AI and forecasting models
- CI/CD and MLOps
- Monitoring and cost control
Comprehensive package
For companies that can call the play and need the whole line to execute it.
- AI solutions
- Delivery pipeline
- Forecasting and capacity models
- Technical support
- Architecture on call
Questions
Before you ask
That is the position we are playing from. Give us the history and we model the range of outcomes, put numbers against each one, and tell you which decision holds up across all of them.
Yes. Tell us how the data can be reached and in what shape (SSH, FTP, SQL, .db, API, CSV) and we build the ingestion around it rather than asking you to change anything.
Yes. Data models, mapped and deduplicated records, identity matching, web scraped collections, and the pipeline that keeps all of it fresh after we leave.
Yes. Send the problem and we walk it end to end, from the electricity in the rack to the dashboard your team opens on Monday.
No. Most engagements start with a hunch and a data sample. Writing the plan down is one of the first things we hand over.
None of it, in most cases. We work against what you already have, in the shape it is already in, and copy only what a specific step needs.
Nothing dramatic. It keeps running in your accounts, documented and monitored, with every assumption written down so the next team can argue with them.
Contact
Send the problem
One paragraph is enough: what you have, what it should do, when you need it.
Send it with where the data lives and in what shape. The first reply comes back with a plan in it, and a number attached.